The current Global Navigation Satellite System (GNSS) receiver acquisition and tracking functions are vulnerable due to the extreme low Signal-to-Noise Ratio (SNR) on which the satellite signals are received. The degradation of GNSS signals can occur due to many reasons. For example, the satellite signals can be attenuated due to shadowing, or an intentional or unintentional interference can bury the signals under interference.
Especially for aviation GNSS receivers, the integrity monitoring of GNSS signals is one of the most important and challenging tasks. The current integrity monitoring scheme for an aviation GNSS receiver assumes that there is no interference present at the input to the GNSS receiver. However, there have been several reported incidents where this assumption did not hold. This can lead to overoptimistic estimation of pseudorange errors and cause severe problems in maintaining the required integrity and accuracy levels.
A significantly large number of signals from different satellite constellations and frequencies will be available for future Multi-Constellation Multi-Frequency (MCMF) GNSS receivers. This will provide future receivers with considerable flexibility when selecting which signals to include for the position and velocity computations, for example, by selecting signals that are not being affected by external interference. Alternatively, the receiver can de-weight signals when it detects the signals are being subjected to interference. Interference awareness is also the first step of interference mitigation.
Signal monitoring requires different techniques in order to detect whether external interference is degrading the GNSS receiver performance. One way to monitor the quality of the incoming data from the Radio Frequency Front End (RF-FE) is to transform the data into frequency domain and to look for any anomalies. Typically, many interference sources, such as harmonics from other transmitters, are clearly visible in the frequency domain, which makes the signal quality monitoring much easier.
The most common technique to perform this type of spectrum monitoring is to have a dedicated Fast Fourier Transform (FFT) engine designed to do the translation of the incoming samples into the frequency domain. However, this approach needs additional design and verification efforts, which will lead to larger designs with increased recurring and non-recurring costs.